Resource Allocation for Mobile Metaverse with the Internet of Vehicles over 6G Wireless Communications: A Deep Reinforcement Learning Approach
Not on the map. Why?
Abstract (arXiv)
Improving the interactivity and interconnectivity between people is one of the highlights of the Metaverse. The Metaverse relies on a core approach, digital twinning, which is a means to replicate physical world objects, people, actions and scenes onto the virtual world. Being able to access scenes and information associated with the physical world, in the Metaverse in real-time and under mobility, is essential in developing a highly accessible, interactive and interconnective experience for all users. This development allows users from other locations to access high-quality real-world and up-to-date information about events happening in another location, and socialize with others hyper-interactively. Nevertheless, receiving continual, smooth updates generated by others from the Metaverse is a challenging task due to the large data size of the virtual world graphics and the need for low latency transmission. With the development of Mobile Augmented Reality (MAR), users can interact via the Metaverse in a highly interactive manner, even under mobility. Hence in our work, we considered an environment with users in moving Internet of Vehicles (IoV), downloading real-time virtual world updates from Metaverse Service Provider Cell Stations (MSPCSs) via wireless communications. We design an environment with multiple cell stations, where there will be a handover of users' virtual world graphic download tasks between cell stations. As transmission latency is the primary concern in receiving virtual world updates under mobility, our work aims to allocate system resources to minimize the total time taken for users in vehicles to download their virtual world scenes from the cell stations. We utilize deep reinforcement learning and evaluate the performance of the algorithms under different environmental configurations. Our work provides a use case of the Metaverse over AI-enabled 6G communications.
Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Builds on 14 of its 33 references that are in the map. First posted on arXiv 27 Sep 2022.
Builds on
14 of 33 references.
References · 14
- 2014Adam: A Method for Stochastic Optimizationcited by 37,240
- 2017Proximal Policy Optimization Algorithmscited by 15,215
- 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,885
- 2015Continuous control with deep reinforcement learningcited by 3,407
- 2016Asynchronous Methods for Deep Reinforcement Learningcited by 2,258
- 2015Dueling Network Architectures for Deep Reinforcement Learningcited by 785
- 2021All One Needs to Know about Metaverse: A Complete Survey on Technological Singularity, Virtual Ecosystem, and Research Agendacited by 67
- 2015Efficient Multi-User Computation Offloading for Mobile-Edge Cloud Computingcited by 28
- 2022MetaFi: Device-Free Pose Estimation via Commodity WiFi for Metaverse Avatar Simulationcited by 22
- 2022When Internet of Things meets Metaverse: Convergence of Physical and Cyber Worldscited by 19
- 2021Wireless Edge-Empowered Metaverse: A Learning-Based Incentive Mechanism for Virtual Realitycited by 12
- 2021A Dynamic Resource Allocation Framework for Synchronizing Metaverse with IoT Service and Datacited by 11
Built on by
Citing papers per year: 2022: 1; 2023: 5.
6 papers.
- 2022Mobile Edge Computing, Metaverse, 6G Wireless Communications, Artificial Intelligence, and Blockchain: Survey and Their Convergencecited by 3
- 2023The Metaverse: Survey, Trends, Novel Pipeline Ecosystem & Future Directionscited by 3
- 2023User-centric Heterogeneous-action Deep Reinforcement Learning for Virtual Reality in the Metaverse over Wireless Networkscited by 1
- 2023Dynamic Partial Computation Offloading for the Metaverse in In-Network Computingcited by 1
- 2023UAV aided Metaverse over Wireless Communications: A Reinforcement Learning Approachcited by 0
- 2023Mobile Edge Adversarial Detection for Digital Twinning to the Metaverse with Deep Reinforcement Learningcited by 0
- 2023Dynamic Partial Computation Offloading for the Metaverse in In-Network Computing9 Jun 2023
- 2023The Metaverse: Survey, Trends, Novel Pipeline Ecosystem & Future Directions18 Apr 2023
- 2023Mobile Edge Adversarial Detection for Digital Twinning to the Metaverse with Deep Reinforcement Learning18 Mar 2023
- 2023User-centric Heterogeneous-action Deep Reinforcement Learning for Virtual Reality in the Metaverse over Wireless Networks3 Feb 2023
- 2023UAV aided Metaverse over Wireless Communications: A Reinforcement Learning Approach4 Jan 2023
- 2022Mobile Edge Computing, Metaverse, 6G Wireless Communications, Artificial Intelligence, and Blockchain: Survey and Their Convergence28 Sep 2022